Proceedings Article10.1109/ICPP.2006.32
Data-Flow Analysis for MPI Programs
Michelle Mills Strout,Barbara Kreaseck,Paul D. Hovland +2 more
- 14 Aug 2006
- pp 175-184
TL;DR: Using the MPI-ICFG data-flow analysis framework improves the precision of activity analysis and as a result significantly reduces memory requirements for the automatically differentiated versions of a set of parallel benchmarks, including some of the NAS parallel benchmarks.
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Abstract: Message passing via MPI is widely used in single-program, multiple-data (SPMD) parallel programs. Existing data-flow frameworks do not model the semantics of message-passing SPMD programs, which can result in less precise and even incorrect analysis results. We present a data-flow analysis framework for performing interprocedural analysis of message-passing SPMD programs. The framework is based on the MPI-ICFG representation, which is an interprocedural control-flow graph (ICFG) augmented with communication edges between possible send and receive pairs and partial context sensitivity. We show how to formulate nonseparable data-flow analyses within our framework using reaching constants as a canonical example. We also formulate and provide experimental results for the nonseparable analysis, activity analysis. Activity analysis is a domain-specific analysis used to reduce the computation and storage requirements for automatically differentiated MPI programs. Automatic differentiation is important for application domains such as climate modeling, electronic device simulation, oil reservoir simulation, medical treatment planning and computational economics to name a few. Our experimental results show that using the MPI-ICFG data-flow analysis framework improves the precision of activity analysis and as a result significantly reduces memory requirements for the automatically differentiated versions of a set of parallel benchmarks, including some of the NAS parallel benchmarks
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Citations
Probabilistic diagnosis of performance faults in large-scale parallel applications
Ignacio Laguna,Dong H. Ahn,Bronis R. de Supinski,Saurabh Bagchi,Todd Gamblin +4 more
- 19 Sep 2012
TL;DR: A novel, highly scalable tool that probabilistically infers the least progressed task in MPI programs using Markov models of execution history and dependence analysis and can isolate the root cause of a particularly perplexing bug encountered at scale in a molecular dynamics simulation.
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A Flexible Architecture for Building Data Flow Analyzers
Matthew B. Dwyer,Lori A. Clarke +1 more
- 01 Aug 1995
TL;DR: An architecture that facilitates the rapid prototyping of data flow analyzers and allows developers to investigate quickly and easily a wide variety of analyzer design alternatives and to understand the practical design tradeoffs better.
15
Hybrid approach for data-flow analysis of MPI programs
Sriram Aananthakrishnan,Greg Bronevetsky,Ganesh Gopalakrishnan +2 more
- 10 Jun 2013
TL;DR: Hybrid methods combining static and dynamic techniques are needed in building the parallel control-flow graph which can then be used to leverage the precision of data-flow analyses for MPI programs.
14
Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation
01 Nov 2022
TL;DR: Enzyme as mentioned in this paper proposes a scheme for differentiating arbitrary DAG-based parallelism that preserves scalability and efficiency, implemented into the LLVM-based Enzyme automatic differentiation framework.
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Depth Analysis of MPI Programs
Barbara Kreaseck,Michelle Mills Strout,Paul D. Hovland +2 more
- 01 Jan 2010
TL;DR: The graph depth is clearly the dominating factor and provides a close approximation to the complexity of iterative data-flow analysis over MPI programs and the number of iterations over the flow graph is bounded by the lattice height multiplied by the graph depth.
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